class_docstr: Test to verify PruningSchedule behavior for step parameters.
  
  This is a parameterized test which runs over all PruningSchedule classes
  to ensure they validate parameters such as begin_step, end_step and frequency
  properly and also apply them correctly during execution.
code: def _validate_sparsity(self, schedule_construct_fn):
      # Should not be < 0.0
      with self.assertRaises(ValueError):
          schedule_construct_fn(-0.001)
      with self.assertRaises(ValueError):
          schedule_construct_fn(-1.0)
      with self.assertRaises(ValueError):
          schedule_construct_fn(-10.0)
  
      # Should not be >= 1.0
      with self.assertRaises(ValueError):
          schedule_construct_fn(1.0)
      with self.assertRaises(ValueError):
          schedule_construct_fn(10.0)
  
      schedule_construct_fn(0.0)
      schedule_construct_fn(0.001)
      schedule_construct_fn(0.5)
      schedule_construct_fn(0.99)
  
imports: from absl.testing import parameterized
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import compat
  from tensorflow_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
label_desc: Use image node_id 6 for calling the PruningScheduleTest obj's underlying member method code with example usage: obj._validate_sparsity(schedule_construct_fn) without return types